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Data analytics helps basketball teams to create tactics. However, manual datacollection and analytics are costly and ineffective. Therefore, we applied adeep bidirectional long short-term memory (BLSTM) and mixture density network(MDN) approach. This model is not only capable of predicting a basketballtrajectory based on real data, but it also can generate new trajectory samples.It is an excellent application to help coaches and players decide when andwhere to shoot. Its structure is particularly suitable for dealing with timeseries problems. BLSTM receives forward and backward information at the sametime, while stacking multiple BLSTMs further increases the learning ability ofthe model. Combined with BLSTMs, MDN is used to generate a multi-modaldistribution of outputs. Thus, the proposed model can, in principle, representarbitrary conditional probability distributions of output variables. We testedour model with two experiments on three-pointer datasets from NBA SportVu data.In the hit-or-miss classification experiment, the proposed model outperformedother models in terms of the convergence speed and accuracy. In the trajectorygeneration experiment, eight model-generated trajectories at a given timeclosely matched real trajectories.

Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction